Energy challenges for ICT

The energy consumption from the expanding use of information and communications technology (ICT) is unsustainable with present drivers, and it will impact heavily on the future climate change. However, ICT devices have the potential to contribute signi - cantly to the reduction of CO2 emission and enhance resource e ciency in other sectors, e.g., transportation (through intelligent transportation and advanced driver assistance systems and self-driving vehicles), heating (through smart building control), and manu- facturing (through digital automation based on smart autonomous sensors). To address the energy sustainability of ICT and capture the full potential of ICT in resource e - ciency, a multidisciplinary ICT-energy community needs to be brought together cover- ing devices, microarchitectures, ultra large-scale integration (ULSI), high-performance computing (HPC), energy harvesting, energy storage, system design, embedded sys- tems, e cient electronics, static analysis, and computation. In this chapter, we introduce challenges and opportunities in this emerging eld and a common framework to strive towards energy-sustainable ICT

Multi-Sensor Context-Awareness in Mobile Devices and Smart Artefacts

The use of context in mobile devices is receiving increasing attention in mobile and ubiquitous computing research. In this article we consider how to augment mobile devices with awareness of their environment and situation as context. Most work to date has been based on integration of generic context sensors, in particular for location and visual context. We propose a different approach based on integration of multiple diverse sensors for awareness of situational context that can not be inferred from location, and targeted at mobile device platforms that typically do not permit processing of visual context. We have investigated multi-sensor context-awareness in a series of projects, and report experience from development of a number of device prototypes. These include development of an awareness module for augmentation of a mobile phone, of the Mediacup exemplifying context-enabled everyday artifacts, and of the Smart-Its platform for aware mobile devices. The prototypes have been explored in various applications to validate the multi-sensor approach to awareness, and to develop new perspectives of how embedded context-awareness can be applied in mobile and ubiquitous computing

Modeling and visualizing networked multi-core embedded software energy consumption

In this report we present a network-level multi-core energy model and a software development process workflow that allows software developers to estimate the energy consumption of multi-core embedded programs. This work focuses on a high performance, cache-less and timing predictable embedded processor architecture, XS1. Prior modelling work is improved to increase accuracy, then extended to be parametric with respect to voltage and frequency scaling (VFS) and then integrated into a larger scale model of a network of interconnected cores. The modelling is supported by enhancements to an open source instruction set simulator to provide the first network timing aware simulations of the target architecture. Simulation based modelling techniques are combined with methods of results presentation to demonstrate how such work can be integrated into a software developer's workflow, enabling the developer to make informed, energy aware coding decisions. A set of single-, multi-threaded and multi-core benchmarks are used to exercise and evaluate the models and provide use case examples for how results can be presented and interpreted. The models all yield accuracy within an average +/-5 % error margin

An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics

Near-sensor data analytics is a promising direction for IoT endpoints, as it minimizes energy spent on communication and reduces network load - but it also poses security concerns, as valuable data is stored or sent over the network at various stages of the analytics pipeline. Using encryption to protect sensitive data at the boundary of the on-chip analytics engine is a way to address data security issues. To cope with the combined workload of analytics and encryption in a tight power envelope, we propose Fulmine, a System-on-Chip based on a tightly-coupled multi-core cluster augmented with specialized blocks for compute-intensive data processing and encryption functions, supporting software programmability for regular computing tasks. The Fulmine SoC, fabricated in 65nm technology, consumes less than 20mW on average at 0.8V achieving an efficiency of up to 70pJ/B in encryption, 50pJ/px in convolution, or up to 25MIPS/mW in software. As a strong argument for real-life flexible application of our platform, we show experimental results for three secure analytics use cases: secure autonomous aerial surveillance with a state-of-the-art deep CNN consuming 3.16pJ per equivalent RISC op; local CNN-based face detection with secured remote recognition in 5.74pJ/op; and seizure detection with encrypted data collection from EEG within 12.7pJ/op.Comment: 15 pages, 12 figures, accepted for publication to the IEEE Transactions on Circuits and Systems - I: Regular Paper

Education and Research Integration of Emerging Multidisciplinary Medical Devices Security

Traditional embedded systems such as secure smart cards and nano-sensor networks have been utilized in various usage models. Nevertheless, emerging secure deeply-embedded systems, e.g., implantable and wearable medical devices, have comparably larger “attack surface”. Specifically, with respect to medical devices, a security breach can be life-threatening (for which adopting traditional solutions might not be practical due to tight constraints of these often-battery-powered systems), and unlike traditional embedded systems, it is not only a matter of financial loss. Unfortunately, although emerging cryptographic engineering research mechanisms for such deeply-embedded systems have started solving this critical, vital problem, university education (at both graduate and undergraduate level) lags comparably. One of the pivotal reasons for such a lag is the multi-disciplinary nature of the emerging security bottlenecks. Based on the aforementioned motivation, in this work, at Rochester Institute of Technology, we present an effective research and education integration strategy to overcome this issue in one of the most critical deeply-embedded systems, i.e., medical devices. Moreover, we present the results of two years of implementation of the presented strategy at graduate-level through fault analysis attacks, a variant of side-channel attacks. We note that the authors also supervise an undergraduate student and the outcome of the presented work has been assessed for that student as well; however, the emphasis is on graduate-level integration. The results of the presented work show the success of the presented methodology while pinpointing the challenges encountered compared to traditional embedded system security research/teaching integration of medical devices security. We would like to emphasize that our integration approaches are general and scalable to other critical infrastructures as well

Multidisciplinary Approaches and Challenges in Integrating Emerging Medical Devices Security Research and Education

Traditional embedded systems such as secure smart cards and nano-sensor networks have been utilized in various usage models. Nevertheless, emerging secure deeply-embedded systems, e.g., implantable and wearable medical devices, have comparably larger “attack surface”. Specifically, with respect to medical devices, a security breach can be life-threatening (for which adopting traditional solutions might not be practical due to tight constraints of these often-battery-powered systems), and unlike traditional embedded systems, it is not only a matter of financial loss. Unfortunately, although emerging cryptographic engineering research mechanisms for such deeply-embedded systems have started solving this critical, vital problem, university education (at both graduate and undergraduate level) lags comparably. One of the pivotal reasons for such a lag is the multi-disciplinary nature of the emerging security bottlenecks. Based on the aforementioned motivation, in this work, at Rochester Institute of Technology, we present an effective research and education integration strategy to overcome this issue in one of the most critical deeply-embedded systems, i.e., medical devices. Moreover, we present the results of two years of implementation of the presented strategy at graduate-level through fault analysis attacks, a variant of side-channel attacks. We note that the authors also supervise an undergraduate student and the outcome of the presented work has been assessed for that student as well; however, the emphasis is on graduate-level integration. The results of the presented work show the success of the presented methodology while pinpointing the challenges encountered compared to traditional embedded system security research/teaching integration of medical devices security. We would like to emphasize that our integration approaches are general and scalable to other critical infrastructures as well

Dependable Digitally-Assisted Mixed-Signal IPs Based on Integrated Self-Test & Self-Calibration

Heterogeneous SoC devices, including sensors, analogue and mixed-signal front-end circuits and the availability of massive digital processing capability, are being increasingly used in safety-critical applications like in the automotive, medical, and the security arena. Already a significant amount of attention has been paid in literature with respect to the dependability of the digital parts in heterogeneous SoCs. This is in contrast to especially the sensors and front-end mixed-signal electronics; these are however particular sensitive to external influences over time and hence determining their dependability. This paper provides an integrated SoC/IP approach to enhance the dependability. It will give an example of a digitally-assisted mixed-signal front-end IP which is being evaluated under its mission profile of an automotive tyre pressure monitoring system. It will be shown how internal monitoring and digitally-controlled adaptation by using embedded processors can help in terms of improving the dependability of this mixed-signal part under harsh conditions for a long time